Agentic
Like-for-like- GLM-4.7
- 45.7
- Inkling-Small
- 70.1
- Weighted basis
- 2 vs 2 rows
- Reading
- Inkling-Small leads
Model comparison
Updated July 30, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
5 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.
Tool use, computer use, and multi-step task completion
Inkling-Small
Inkling-Small leads on the same 2 weighted benchmark rows.
Confidence: limited
Prompts that approach the documented context limit
Inkling-Small
Inkling-Small has the larger documented context window.
Confidence: documented
Code generation, repair, and software-engineering tasks
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. GLM-4.7 does not fit this workload in one request. GLM-4.7 has no comparable published API token rate.
Confidence: listed-rates
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
2 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
| Category | GLM-4.7 | Inkling-Small | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 45.7 | 70.1 | Like-for-like2 vs 2 rows | Inkling-Small leads |
| Coding | 75.4 | 62.4 | Directional only3 vs 3 rows | Directional only |
| Knowledge | 51.8 | 53.4 | Directional only3 vs 2 rows | Directional only |
| Reasoning | Not measured | 40.1 | Not comparable0 vs 1 rows | Not comparable |
| Math | 1.8 | 92.9 | Not comparable2 vs 2 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | 76.6 | Not comparable0 vs 2 rows | Not comparable |
| Instruction following | Not measured | 82.2 | Not comparable0 vs 1 rows | Not comparable |
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.
BrowseComp
Agentic
Terminal-Bench 2.0
Agentic
HLE
Knowledge
SWE-bench Verified
Coding
GPQA
Knowledge
Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.
1K fresh input + 500 output tokens
GLM-4.7 has no comparable published API token rate.
50K fresh input + 3K output tokens
GLM-4.7 has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
GLM-4.7 does not fit this workload in one request. GLM-4.7 has no comparable published API token rate.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
GLM-4.7
200K
Inkling-Small
1M
GLM-4.7
Not sourced
Inkling-Small
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GLM-4.7
No comparable hosted API rate
Inkling-Small
$0.116 per 1M cached input tokens
GLM-4.7
Not sourced
Inkling-Small
Not sourced
GLM-4.7
Not sourced
Inkling-Small
Not sourced
GLM-4.7
Not sourced
Inkling-Small
Not sourced
GLM-4.7
Reasoning
Inkling-Small
Hybrid
GLM-4.7
Open Weight
Inkling-Small
Open Weight
GLM-4.7
Open Weight
Inkling-Small
Open Weight
GLM-4.7
2025-10-01
Inkling-Small
2026-07-30
Run the same representative tasks against both endpoints before changing production traffic.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.0
Inkling-Small leads this result
BrowseComp
Inkling-Small leads this result
VITA-Bench
Not directly comparable
Gert Labs
Not directly comparable
MCP Atlas
Not directly comparable
Toolathlon-Verified
Not directly comparable
SWE-bench Verified
Inkling-Small leads this result
LiveCodeBench
Not directly comparable
SWE-Rebench
Not directly comparable
SWE-bench Pro
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
SciCode
Not directly comparable
GPQA
Inkling-Small leads this result
MMLU-Pro
Not directly comparable
HLE
Inkling-Small leads this result
GPQA-D
Not directly comparable
HLE w/o tools
Not directly comparable
AIME 2025
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Not directly comparable
FrontierMath v2 (Tier 4)
Not directly comparable
AIME26
Not directly comparable
HMMT Feb 2026
Not directly comparable
IFBench
Not directly comparable
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Inkling-Small leads the like-for-like agentic tasks comparison across 2 shared weighted benchmark rows.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Inkling-Small has the larger documented context window: 1M, compared with 200K.
Last updated July 30, 2026
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